Tagged articles

sample efficiency

5 articles · Page 1 of 1

Jul 16, 2026 · Artificial Intelligence

FAST: A Parallel Framework that Accelerates Reinforcement Learning for Autonomous Driving

The paper introduces FAST, a parallel reinforcement‑learning sampling framework for autonomous‑driving that decouples individual episode termination from global resets via Dynamic Parallel Sampling Alignment and Scaled Mask‑Padding Optimization, achieving up to 9.08× higher sampling throughput and up to 2× faster training while preserving zero policy loss.

Simulationautonomous drivingparallel computing

0 likes · 15 min read

FAST: A Parallel Framework that Accelerates Reinforcement Learning for Autonomous Driving

Machine Heart

Apr 10, 2026 · Artificial Intelligence

How a Chinese Company Swept the Embodied Intelligence Olympics with Faster, Precise, Low‑Data Robotics

A Chinese robotics firm leveraged a self‑developed VLA model to win all three core tasks at Benjie’s Embodied Intelligence Olympics—peeling oranges, unlocking doors, and flipping socks—outperforming the industry leader Physical Intelligence by up to 35% faster speed, using 30% fewer samples and achieving higher precision in real‑world, fully autonomous scenarios.

VLA modelbenchmark competitionembodied AI

0 likes · 16 min read

How a Chinese Company Swept the Embodied Intelligence Olympics with Faster, Precise, Low‑Data Robotics

Data Party THU

Nov 4, 2025 · Artificial Intelligence

Why Evolution Strategies Beat Reinforcement Learning for Large‑Model Fine‑Tuning

This article reviews the paper “Evolution Strategies at Scale: LLM Fine‑Tuning Beyond Reinforcement Learning”, explaining how parameter‑space exploration via ES provides more stable, sample‑efficient, and reproducible fine‑tuning for billion‑parameter LLMs such as Qwen‑2.5 and LLaMA‑3, and detailing the algorithmic and engineering innovations that make full‑parameter ES practical.

Evolution StrategiesParameter Space OptimizationScalable Training

0 likes · 15 min read

Why Evolution Strategies Beat Reinforcement Learning for Large‑Model Fine‑Tuning

Data Party THU

Oct 15, 2025 · Artificial Intelligence

Designing Safe, Sample-Efficient, and Robust Reinforcement Learning for Ranking and Diffusion Models

This paper proposes a reinforcement‑learning framework that simultaneously ensures safety, sample efficiency, and robustness, applying a contextual‑bandit perspective to ranking/recommendation systems and text‑to‑image diffusion models, and introduces novel algorithms for safe deployment, variance‑reduced off‑policy estimation, and a LOOP method for generative RL.

RobustnessSafetycontextual bandits

0 likes · 5 min read

Designing Safe, Sample-Efficient, and Robust Reinforcement Learning for Ranking and Diffusion Models

DataFunSummit

Jul 8, 2024 · Artificial Intelligence

World Models and Causal Inference in Reinforcement Learning: A Comprehensive Overview

This article reviews the role of world (mental) models and causal inference in reinforcement learning, covering their theoretical foundations, model‑based RL frameworks such as Dyna, sample‑efficiency challenges, causal structure learning, distribution correction, dynamics‑reward modeling, and experimental results that demonstrate performance gains across multiple tasks.

World Modelscausal inferencemodel-based RL

0 likes · 21 min read

World Models and Causal Inference in Reinforcement Learning: A Comprehensive Overview